Datasets:

as3eem
/

IndieMH

	---
	license: cc-by-nc-4.0
	task_categories:
	- text-classification
	- token-classification
	language:
	- en
	- hi
	pretty_name: IndieMH
	size_categories:
	- 10K<n<100K
	tags:
	- mental-health
	- counseling
	- emotion-recognition
	- dialogue-acts
	- code-switching
	- code-mixing
	- hindi-english
	- therapy
	- conversation
	extra_gated_prompt: 'This dataset contains transcripts of real mental-health therapy
	sessions and is shared strictly for non-commercial academic research.

	By requesting access you agree to: (1) use the data only for non-commercial research
	purposes, (2) not attempt to re-identify any individual in the dataset, (3) store
	the data securely and not redistribute it without permission, (4) cite the original
	work in any publication that uses this dataset, (5) not use this dataset for any
	clinical, diagnostic, or therapeutic application.'
	extra_gated_fields:
	Name: text
	Affiliation: text
	Intended use: text
	I have read and agree to the terms above: checkbox
	---

	# IndieMH: Code-Mixed Mental Health Counseling Conversations

	## Dataset Summary

	IndieMH is an extended, enriched version of the publicly available [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request) (published at WSDM 2022). It augments the original HOPE counseling transcripts with two new layers of annotation introduced in our LREC 2026 paper: code-mixed Hindi–English renderings of every utterance and multi-label emotion annotations (up to four co-occurring emotions per turn). The structural annotations carried forward from HOPE — speaker role, dialogue acts, and conversation sub-topics — are described in the [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request).

	This dataset was introduced in:

	> Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations
	> Aseem Srivastava, Kushagra Mittal, Anusha Tiwari and Md. Shad Akhtar
	> Proceedings of LREC 2026
	> Paper link and citation: to be released

	---

	## Dataset Description

	### Background

	Effective counseling is often best achieved in a client's preferred language, allowing better emotional resonance. Despite this, most existing research in emotion recognition in counseling focuses predominantly on English, overlooking the rich emotional and linguistic complexities of other widely spoken languages. Hinglish — a code-mixed blend of Hindi and English — is one such underexplored linguistic medium that millions use to express their emotions authentically.

	To address this gap, IndieMH lays a foundational step in developing a mental-health conversation dataset in code-mixed Hinglish. Counseling conversations from publicly available sources (the [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request), WSDM 2022) are manually translated into Hinglish. The dataset is employed for the emotion classification task for counseling patients, annotated with 13 emotional states organised under 3 broad emotion categories following an exhaustive annotation guideline. Rigorous sanity checks ensure that the quality of IndieMH adheres to research standards.

	Alongside the dataset, we propose Healer — a novel knowledge-cum-hierarchy aware method for counseling emotion classification in the Hinglish language — benchmarked against 11 baseline methods using accuracy, weighted-F1, and weighted-precision.

	### Relationship to the HOPE Dataset

	\| \| HOPE (WSDM 2022) \| IndieMH (LREC 2026) \|
	\|---\|:---:\|:---:\|
	\| Utterance transcripts \| ✅ \| ✅ (carried forward) \|
	\| Speaker labels (T / P) \| ✅ \| ✅ (carried forward) \|
	\| Dialogue act labels \| ✅ \| ✅ (carried forward) \|
	\| Sub-topic labels \| ✅ \| ✅ (carried forward) \|
	\| Code-mixed renderings \| ❌ \| ✅ new \|
	\| Emotion labels (multi-label) \| ❌ \| ✅ new \|

	The HOPE dataset is available via access request at: https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request

	### Languages

	\| Language \| Role \|
	\|---\|---\|
	\| English \| Primary utterance language \|
	\| Hindi (Devanagari & Romanized) \| Code-mixed renderings \|

	---

	## Dataset Structure

	### Splits

	\| Split \| Conversations \| Utterances \| Avg. turns/conv \|
	\|-------\|:---:\|:---:\|:---:\|
	\| train \| 130 \| 8,322 \| 64.0 \|
	\| test \| 39 \| 2,010 \| 51.5 \|
	\| validation \| 21 \| 1,191 \| 56.7 \|
	\| total \| 190 \| 11,523 \| 60.6 \|

	### Data Fields

	\| Field \| Type \| Source \| Description \|
	\|---\|---\|:---:\|---\|
	\| `ID` \| string \| HOPE \| Unique turn identifier in `{conversation_id}_{turn_index}` format (e.g. `108_0`) \|
	\| `Speaker` \| string \| HOPE \| `T` = Therapist · `P` = Patient \|
	\| `Utterance` \| string \| HOPE \| Original English utterance \|
	\| `Dialogue_Act` \| string \| HOPE \| Dialogue act label (see table below) \|
	\| `Sub topic` \| string \| HOPE \| Conversation sub-topic label (see table below) \|
	\| `Code-Mix` \| string \| IndieMH \| Code-mixed Hindi–English rendering of the utterance (new in this work) \|
	\| `Emotion` \| string \| IndieMH \| Comma-separated emotion label(s); up to four co-occurring labels per utterance (new in this work) \|

	### Annotation Schema

	#### Emotion Labels

	Utterances may carry up to four co-occurring emotion labels drawn from a Plutchik-inspired taxonomy:

	`Neutral` · `Sadness` · `Scared` · `Annoyance` · `Confusion` · `Anticipation` · `Contempt` · `Joy` · `Trust` · `Serenity` · `Disgust` · `Anger` · `Surprise`

	\| Emotion \| Occurrences \| % of labelled instances \|
	\|---\|:---:\|:---:\|
	\| Neutral \| 8,960 \| 73.8% \|
	\| Sadness \| 1,222 \| 10.1% \|
	\| Scared \| 568 \| 4.7% \|
	\| Annoyance \| 364 \| 3.0% \|
	\| Confusion \| 339 \| 2.8% \|
	\| Anticipation \| 202 \| 1.7% \|
	\| Contempt \| 98 \| 0.8% \|
	\| Joy \| 92 \| 0.8% \|
	\| Others \| 294 \| 2.4% \|

	#### Dialogue Act Labels

	\| Label \| Description \|
	\|---\|---\|
	\| `id` \| Information delivery \|
	\| `irq` \| Information request \|
	\| `gc` \| General chat \|
	\| `crq` \| Clarification request \|
	\| `cd` \| Clarification delivery \|
	\| `ack` \| Acknowledgement \|
	\| `yq` \| Yes/no question \|
	\| `gt` \| Greeting \|
	\| `on` \| Opinion/narrative \|
	\| `od` \| Opinion delivery \|
	\| `op` \| Opinion/perspective \|
	\| `orq` \| Opinion request \|
	\| `cr` \| Confirmation request \|
	\| `cv` \| Confirmation \|
	\| `ci` \| Closing \|

	#### Sub-Topic Labels

	\| Label \| Description \| % of utterances \|
	\|---\|---\|:---:\|
	\| `routine` \| Day-to-day activities and coping strategies \| 47.1% \|
	\| `inactive` \| Opening/closing exchanges, off-topic turns \| 21.4% \|
	\| `symp/reasoning` \| Symptom discussion and clinical reasoning \| 20.6% \|
	\| `story` \| Personal narrative and life events \| 10.8% \|

	### Dataset Statistics

	\| Metric \| Value \|
	\|---\|---\|
	\| Total utterances \| 11,523 \|
	\| Unique conversations \| 190 \|
	\| Avg. utterance length (words) \| 22.0 \|
	\| Median utterance length (words) \| 11 \|
	\| Max utterance length (words) \| 799 \|
	\| Speaker balance (T / P) \| 50.4% / 49.6% \|
	\| Utterances with ≥2 emotion labels \| 554 (4.8%) \|

	---

	## Access

	This dataset is gated. The dataset page and sample previews are publicly visible. To download the full data, you must agree to the terms of use. Access is granted automatically upon agreement.

	---

	## Citation

	If you use IndieMH, please cite both this work and the original HOPE dataset it extends.

	This work (LREC 2026):

	> Paper link and BibTeX citation will be released upon publication.

	Aseem Srivastava, Kushagra Mittal, Anusha Tiwari and Md. Shad Akhtar. "Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations." Proceedings of LREC 2026.

	Original HOPE dataset (WSDM 2022):

	Please refer to the [HOPE dataset repository](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request) for the BibTeX entry of the original WSDM 2022 paper.

	---

	## License

	This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license.

	---
	license: cc-by-nc-4.0
	task_categories:
	- text-classification
	- token-classification
	language:
	- en
	- hi
	pretty_name: IndieMH
	size_categories:
	- 10K<n<100K
	tags:
	- mental-health
	- counseling
	- emotion-recognition
	- dialogue-acts
	- code-switching
	- code-mixing
	- hindi-english
	- therapy
	- conversation
	extra_gated_prompt: 'This dataset contains transcripts of real mental-health therapy
	sessions and is shared strictly for non-commercial academic research.

	By requesting access you agree to: (1) use the data only for non-commercial research
	purposes, (2) not attempt to re-identify any individual in the dataset, (3) store
	the data securely and not redistribute it without permission, (4) cite the original
	work in any publication that uses this dataset, (5) not use this dataset for any
	clinical, diagnostic, or therapeutic application.'
	extra_gated_fields:
	Name: text
	Affiliation: text
	Intended use: text
	I have read and agree to the terms above: checkbox
	---

	# IndieMH: Code-Mixed Mental Health Counseling Conversations

	## Dataset Summary

	IndieMH is an extended, enriched version of the publicly available [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request) (published at WSDM 2022). It augments the original HOPE counseling transcripts with two new layers of annotation introduced in our LREC 2026 paper: code-mixed Hindi–English renderings of every utterance and multi-label emotion annotations (up to four co-occurring emotions per turn). The structural annotations carried forward from HOPE — speaker role, dialogue acts, and conversation sub-topics — are described in the [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request).

	This dataset was introduced in:

	> Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations
	> Aseem Srivastava, Kushagra Mittal, Anusha Tiwari and Md. Shad Akhtar
	> Proceedings of LREC 2026
	> Paper link and citation: to be released

	---

	## Dataset Description

	### Background

	Effective counseling is often best achieved in a client's preferred language, allowing better emotional resonance. Despite this, most existing research in emotion recognition in counseling focuses predominantly on English, overlooking the rich emotional and linguistic complexities of other widely spoken languages. Hinglish — a code-mixed blend of Hindi and English — is one such underexplored linguistic medium that millions use to express their emotions authentically.

	To address this gap, IndieMH lays a foundational step in developing a mental-health conversation dataset in code-mixed Hinglish. Counseling conversations from publicly available sources (the [HOPE dataset](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request), WSDM 2022) are manually translated into Hinglish. The dataset is employed for the emotion classification task for counseling patients, annotated with 13 emotional states organised under 3 broad emotion categories following an exhaustive annotation guideline. Rigorous sanity checks ensure that the quality of IndieMH adheres to research standards.

	Alongside the dataset, we propose Healer — a novel knowledge-cum-hierarchy aware method for counseling emotion classification in the Hinglish language — benchmarked against 11 baseline methods using accuracy, weighted-F1, and weighted-precision.

	### Relationship to the HOPE Dataset

	\| \| HOPE (WSDM 2022) \| IndieMH (LREC 2026) \|
	\|---\|:---:\|:---:\|
	\| Utterance transcripts \| ✅ \| ✅ (carried forward) \|
	\| Speaker labels (T / P) \| ✅ \| ✅ (carried forward) \|
	\| Dialogue act labels \| ✅ \| ✅ (carried forward) \|
	\| Sub-topic labels \| ✅ \| ✅ (carried forward) \|
	\| Code-mixed renderings \| ❌ \| ✅ new \|
	\| Emotion labels (multi-label) \| ❌ \| ✅ new \|

	The HOPE dataset is available via access request at: https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request

	### Languages

	\| Language \| Role \|
	\|---\|---\|
	\| English \| Primary utterance language \|
	\| Hindi (Devanagari & Romanized) \| Code-mixed renderings \|

	---

	## Dataset Structure

	### Splits

	\| Split \| Conversations \| Utterances \| Avg. turns/conv \|
	\|-------\|:---:\|:---:\|:---:\|
	\| train \| 130 \| 8,322 \| 64.0 \|
	\| test \| 39 \| 2,010 \| 51.5 \|
	\| validation \| 21 \| 1,191 \| 56.7 \|
	\| total \| 190 \| 11,523 \| 60.6 \|

	### Data Fields

	\| Field \| Type \| Source \| Description \|
	\|---\|---\|:---:\|---\|
	\| `ID` \| string \| HOPE \| Unique turn identifier in `{conversation_id}_{turn_index}` format (e.g. `108_0`) \|
	\| `Speaker` \| string \| HOPE \| `T` = Therapist · `P` = Patient \|
	\| `Utterance` \| string \| HOPE \| Original English utterance \|
	\| `Dialogue_Act` \| string \| HOPE \| Dialogue act label (see table below) \|
	\| `Sub topic` \| string \| HOPE \| Conversation sub-topic label (see table below) \|
	\| `Code-Mix` \| string \| IndieMH \| Code-mixed Hindi–English rendering of the utterance (new in this work) \|
	\| `Emotion` \| string \| IndieMH \| Comma-separated emotion label(s); up to four co-occurring labels per utterance (new in this work) \|

	### Annotation Schema

	#### Emotion Labels

	Utterances may carry up to four co-occurring emotion labels drawn from a Plutchik-inspired taxonomy:

	`Neutral` · `Sadness` · `Scared` · `Annoyance` · `Confusion` · `Anticipation` · `Contempt` · `Joy` · `Trust` · `Serenity` · `Disgust` · `Anger` · `Surprise`

	\| Emotion \| Occurrences \| % of labelled instances \|
	\|---\|:---:\|:---:\|
	\| Neutral \| 8,960 \| 73.8% \|
	\| Sadness \| 1,222 \| 10.1% \|
	\| Scared \| 568 \| 4.7% \|
	\| Annoyance \| 364 \| 3.0% \|
	\| Confusion \| 339 \| 2.8% \|
	\| Anticipation \| 202 \| 1.7% \|
	\| Contempt \| 98 \| 0.8% \|
	\| Joy \| 92 \| 0.8% \|
	\| Others \| 294 \| 2.4% \|

	#### Dialogue Act Labels

	\| Label \| Description \|
	\|---\|---\|
	\| `id` \| Information delivery \|
	\| `irq` \| Information request \|
	\| `gc` \| General chat \|
	\| `crq` \| Clarification request \|
	\| `cd` \| Clarification delivery \|
	\| `ack` \| Acknowledgement \|
	\| `yq` \| Yes/no question \|
	\| `gt` \| Greeting \|
	\| `on` \| Opinion/narrative \|
	\| `od` \| Opinion delivery \|
	\| `op` \| Opinion/perspective \|
	\| `orq` \| Opinion request \|
	\| `cr` \| Confirmation request \|
	\| `cv` \| Confirmation \|
	\| `ci` \| Closing \|

	#### Sub-Topic Labels

	\| Label \| Description \| % of utterances \|
	\|---\|---\|:---:\|
	\| `routine` \| Day-to-day activities and coping strategies \| 47.1% \|
	\| `inactive` \| Opening/closing exchanges, off-topic turns \| 21.4% \|
	\| `symp/reasoning` \| Symptom discussion and clinical reasoning \| 20.6% \|
	\| `story` \| Personal narrative and life events \| 10.8% \|

	### Dataset Statistics

	\| Metric \| Value \|
	\|---\|---\|
	\| Total utterances \| 11,523 \|
	\| Unique conversations \| 190 \|
	\| Avg. utterance length (words) \| 22.0 \|
	\| Median utterance length (words) \| 11 \|
	\| Max utterance length (words) \| 799 \|
	\| Speaker balance (T / P) \| 50.4% / 49.6% \|
	\| Utterances with ≥2 emotion labels \| 554 (4.8%) \|

	---

	## Access

	This dataset is gated. The dataset page and sample previews are publicly visible. To download the full data, you must agree to the terms of use. Access is granted automatically upon agreement.

	---

	## Citation

	If you use IndieMH, please cite both this work and the original HOPE dataset it extends.

	This work (LREC 2026):

	> Paper link and BibTeX citation will be released upon publication.

	Aseem Srivastava, Kushagra Mittal, Anusha Tiwari and Md. Shad Akhtar. "Knowledge-Infused Hierarchy-Aware Emotion Recognition in Code-mixed Mental Health Counseling Conversations." Proceedings of LREC 2026.

	Original HOPE dataset (WSDM 2022):

	Please refer to the [HOPE dataset repository](https://github.com/LCS2-IIITD/SPARTA_WSDM2022#hope-dataset-access-request) for the BibTeX entry of the original WSDM 2022 paper.

	---

	## License

	This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license.